Deep Learning Approaches for Zenith Total Delay Estimation

Zenith Total Delay (ZTD) is a crucial parameter for understanding the effects of atmospheric conditions on satellite signals, constituting a fundamental aspect of precision positioning and atmospheric modeling applications. Traditional methods for ZTD estimation, including GNSS observations, numerical weather prediction models, and interpolation techniques, encounter critical limitations such as generalization constraints, sparse data availability, insufficient spatial coverage, high computational costs, and limited adaptability to dynamic atmospheric changes. Deep learning techniques provide substantial benefits, including processing large and complex datasets, enabling dynamic modeling, and delivering rapid and accurate estimations.

This study integrates real-time GNSS observations with high-resolution atmospheric reanalysis data from the ERA5 dataset to develop deep learning-based methods for ZTD estimation. GNSS data were sourced from 17 IGS tropospheric stations strategically selected to represent diverse geographic and climatic conditions. These stations supplied ZTD values and their temporal variations at 5-minute intervals, spanning February 2023 to January 2024. ERA5 data, offering hourly atmospheric parameters, necessitated the alignment of GNSS temporal resolution with ERA5 for spatial modeling. The spatial distribution of GNSS data was optimized using interpolation techniques to enhance the quality of inputs for deep-learning models.

The findings highlight the potential of deep learning techniques to enhance ZTD estimation processes. Future research will focus on integrating additional datasets, such as InSAR, to achieve higher spatial resolution and improved accuracy. Moreover, advanced deep learning architectures, including attention mechanisms, will be investigated to refine estimation methods and broaden their applications in atmospheric and geospatial studies.